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Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania.

Yaovi Mahuton Gildas HounmanouKåre MølbakJonas KählerRobinson H MdegelaJohn E OlsenAnders Dalsgaard
Published in: BMC research notes (2019)
The Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control.
Keyphrases
  • risk factors
  • public health
  • electronic health record
  • south africa
  • healthcare
  • big data
  • machine learning
  • mass spectrometry
  • artificial intelligence
  • data analysis
  • deep learning